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Estimation of gradients and coordinate covariation in classification
Published on Feb 25, 20074409 Views
We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The motivation for the algorithm is to find salient vari
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Chapter list
Estimation of Gradients00:01
Motivation01:52
Motivation02:51
Motivation03:55
Global shrinkage estimators04:08
Global shrinkage estimators04:31
Global shrinkage estimators04:48
Global shrinkage estimators05:05
Global shrinkage estimators05:14
Reproducing Kernel Hilbert Spaces05:33
Reproducing Kernel Hilbert Spaces06:05
Reproducing Kernel Hilbert Spaces06:37
Reproducing Kernel Hilbert Spaces06:42
Classication06:57
Classication07:36
Classication08:55
Learning the gradient09:19
Learning the gradient10:08
Formulating the algorithm10:16
Formulating the algorithm10:43
Elements for algorithm11:19
Elements for algorithm11:26
Elements for algorithm13:21
Gradient algorithms14:23
Remark14:39
Remark14:48
Representer theorems15:57
Representer theorems16:21
Reducing the matrix size16:56
Reducing the matrix size17:03
Convergence to the gradient18:36
Quantities of interest20:29
Quantities of interest20:45
Linear example21:10
Linear example21:31
Linear example22:14
Linear example22:32
Linear example22:58
Nonlinear example23:03
Nonlinear example23:10
Nonlinear example23:34
Nonlinear example23:44
Gene expression data24:00
Gene expression data24:21
Decay of norms25:13
Decay of norms25:28
Decay of norms25:34
Restriction to a manifold26:42
Restriction to a manifold26:53
Restriction to a manifold27:38
Restriction to a manifold27:52
Restriction to a manifold28:17
Restriction to a manifold28:51
Dimensionality reduction29:48
Dimensionality reduction30:11
Dimensionality reduction30:28
Dimensionality reduction30:58
Dimensionality reduction31:45
Dimensionality reduction32:11
Discussion33:15
Discussion34:09
Discussion34:32